🎬📊 Movie Classification Analysis & Prediction

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📌 Project Overview

This project focuses on Movie Classification Analysis & Prediction 🎥 using data science techniques.
It includes:

• 📂 Data Loading & Cleaning
• 🔍 Exploratory Data Analysis (EDA)
• 📊 Data Visualization
• 🤖 Machine Learning Model (Classification)

The goal is to analyze movie-related data and build a model that can predict movie categories/classes based on features.

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📁 Dataset

• Dataset used: Movie_classification.csv
• Contains information about movies such as:
  • 🎭 Genre
  • ⭐ Ratings
  • 💰 Revenue
  • 🎬 Other attributes used for classification

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⚙️ Tech Stack

Tool	Purpose
🐍 Python	Programming
📊 Pandas	Data Manipulation
🔢 NumPy	Numerical Operations
📉 Matplotlib	Basic Visualizations
🎨 Seaborn	Advanced Visualizations
🤖 Scikit-learn	Machine Learning

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🚀 Project Workflow

1️⃣ Import Libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

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2️⃣ Load Dataset 📂

data = pd.read_csv("Movie_classification.csv")
df = data.copy()

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3️⃣ Exploratory Data Analysis 🔍

View dataset structure:

-head()

-tail()

-info()

-describe()

-Understand:

-Missing values ❌

-Data types 🔢

-Feature distributions 📊

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4️⃣ Data Cleaning 🧹

-Handle missing values

-Remove duplicates

-Fix data types

-Feature selection

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5️⃣ Data Visualization 📊

Some key visualizations:

-📈 Distribution plots

-📊 Count plots

-🔥 Heatmaps (correlation)

-📉 Pairplots

Example:

sns.heatmap(df.corr(), annot=True)
plt.show()

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6️⃣ Model Building 🤖

Used Logistic Regression (Binomial) for classification

Steps:

-Split data (Train/Test)

-Train model

-Predict results

-Evaluate performance

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7️⃣ Model Evaluation 📏

Metrics used:

-✅ Accuracy

-📉 Confusion Matrix

-📊 Classification Report

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📌 Key Insights 💡

-🎯 Identified important features affecting classification

-📊 Found correlations between variables

-🤖 Built a predictive model for movie classification

-📉 Improved understanding of dataset patterns

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🧠 Future Improvements

-🔄 Try advanced models (Random Forest, XGBoost)

-⚙️ Hyperparameter tuning

-📊 More feature engineering

-🚀 Deployment as a web app

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📂 Project Structure

📁 Movie-Classification-Project

│── 📄 Movies.ipynb │── 📄 Movie_classification.csv │── 📄 cleaned_movie.csv │── 📄 README.md

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🙌 Author

Krishna Arora 🚀

📊 Data Science Enthusiast

Linkedin:www.linkedin.com/in/krishna-arora-14ba6631b

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💬 “Data is the new oil, but insights are the real fuel!” 🚀